This Python script creates a password-protected ZIP file using the pyzipper library. It allows you to specify the files to include in the ZIP and set a password for encryption. The resulting ZIP file requires the provided password to access its contents, providing an additional layer of security.
Types of PromptsZero-Shot Prompting - Providing a language model with a task without any examples of how to perform it. The model relies on its pre-training to generate a response. Example: asking for a translation without providing previous examples.
Few-Shot Prompting - Giving the model a few examples of the task along with the prompt. These examples guide the model in understanding what's expected and demonstrate the desired output format.
Chain of Thought Prompting - A technique that involves guiding the model through a step-by-step reasoning process. It's useful for complex tasks that require logic or reasoning, like math problems or cause-and-effect questions.
Contextual Importance in LLMs
Guiding Responses - The context provided in prompts ensures that the responses are accurate, detailed, and relevant to the query.
Disambiguation - It helps in clarifying ambiguous terms or phrases, enhancing the model's understanding.
Relevance - The more relevant the context, the more on-point the LLM's output, avoiding generic or off-topic responses.
Effective prompt engineering is about striking a balance – providing enough context to guide the model while keeping the input focused and relevant. This approach enables users to harness the full potential of LLMs, turning them into powerful tools for a wide array of applications in business, research, and creative domains.
Types of Prompts Zero-Shot Prompting - Providing a language model with a task without any examples of how to perform it. The model relies on its pre-training to generate a response. Example: asking for a translation without providing previous examples.
Few-Shot Prompting - Giving the model a few examples of the task along with the prompt. These examples guide the model in understanding what's expected and demonstrate the desired output format.
Chain of Thought Prompting - A technique that involves guiding the model through a step-by-step reasoning process. It's useful for complex tasks that require logic or reasoning, like math problems or cause-and-effect questions.
Contextual Importance in LLMs Guiding Responses - The context provided in prompts ensures that the responses are accurate, detailed, and relevant to the query. Disambiguation - It helps in clarifying ambiguous terms or phrases, enhancing the model's understanding. Relevance - The more relevant the context, the more on-point the LLM's output, avoiding generic or off-topic responses.
https://youtu.be/ApzZ9P8iLa0
Effective prompt engineering is about striking a balance – providing enough context to guide the model while keeping the input focused and relevant. This approach enables users to harness the full potential of LLMs, turning them into powerful tools for a wide array of applications in business, research, and creative domains.